Name
Abstract | ||
|---|---|---|
Cloud computing is rapidly emerging as a new information technology that aims at providing improved efficiency in the private and public sectors, as well as promoting growth, competition, and business dynamism. Cloud computing represents, today, an opportunity also from the perspective of business process analytics since data recorded by process-centered cloud systems can be used to extract information about the underlying processes. Cloud computing architectures can be used in cross-organizational environments in which different organizations execute the same process in different variants and share information about how each variant is executed. If the process is characterized by low predictability and high variability, business rules become the best way to represent the process variants. The contribution of this paper consists in providing: (i) a cloud computing multi-tenancy architecture to support cross-organizational process executions; (ii) an approach for the systematic extraction/composition of distributed data into coherent event logs carrying process-related information of each variant; (iii) the integration of online process mining techniques for the runtime extraction of business rules from event logs representing the process variants running on the infrastructure. The proposed architecture has been implemented and applied for the execution of a real-life process for acknowledging an unborn child performed in four different Dutch municipalities. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/CIDM.2014.7008694 | Computational Intelligence and Data Mining |
Keywords | Field | DocType |
business data processing,cloud computing,data mining,business rules,cloud computing multitenancy architecture,cross-organizational business rules,cross-organizational process executions,event logs,online process mining techniques,process-centered cloud systems,runtime extraction,systematic extraction | Artifact-centric business process model,Data science,Computer science,Artificial intelligence,Business process modeling,Process mining,Business process management,Business process discovery,Business Process Model and Notation,Machine learning,Database,Business rule,Cloud computing | Conference |
Citations | PageRank | References |
0 | 0.34 | 16 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mario Luca Bernardi | 1 | 156 | 29.89 |
| Marta Cimitile | 2 | 183 | 24.34 |
| Fabrizio Maria Maggi | 3 | 90 | 3.84 |